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Award-winning | Used by over 30 universities | Translated into 9 languages

An introduction for everyone. In this rich, fascinating — surprisingly accessible — introduction, leading expert Eric Siegel reveals how predictive analytics works, and how it affects everyone every day. Rather than a “how to” for hands-on techies, the book serves lay readers and experts alike by covering new case studies and the latest state-of-the-art techniques.

Prediction is booming. It reinvents industries and runs the world. Companies, governments, law enforcement, hospitals, and universities are seizing upon the power. These institutions predict whether you're going to click, buy, lie, or die.

How? Prediction is powered by the world's most potent, flourishing unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn. Predictive Analytics unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future drives millions of decisions more effectively, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate.

In this lucid, captivating introduction — now in its Revised and Updated edition — former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction:

What type of mortgage risk Chase Bank predicted before the recession.

Predicting which people will drop out of school, cancel a subscription, or get divorced before they even know it themselves.

Why early retirement predicts a shorter life expectancy and vegetarians miss fewer flights.

Five reasons why organizations predict death — including one health insurance company.

How U.S. Bank and Obama for America calculated — and Hillary for America 2016 plans to calculate — the way to most strongly persuade each individual.

How does predictive analytics work? This jam-packed book satisfies by demystifying the intriguing science under the hood. For future hands-on practitioners pursuing a career in the field, it sets a strong foundation, delivers the prerequisite knowledge, and whets your appetite for more.

A truly omnipresent science, predictive analytics constantly affects our daily lives. Whether you are a consumer of it — or consumed by it — get a handle on the power of Predictive Analytics.

From the Publisher

Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die

Q & A with Author Eric Siegel

No—but Obama did use predictive analytics to help get elected. Nate Silver made election forecasts for each state as a whole: which way would a state trend, overall? In the meantime, the Obama campaign was using predictive analytics to render per-voter predictions. Moving beyond forecasting, true power comes in influencing the future rather than speculating on it—the raison d'être of predictive analytics. Nate Silver publicly competed to win election forecasting, while Obama's analytics team quietly competed to win the election itself. Specifically, team Obama drove per-voter campaign decisions by way of per-vote predictions.

Why does early retirement predict a shorter life expectancy & why do vegetarians miss fewer flights?

These are two more colorful examples of the multitudes of predictive discoveries waiting within data.

University of Zurich discovered that, for a certain working category of males in Austria, each additional year of early retirement decreases life expectancy by 1.8 months. They conjecture that this could be due to unhealthy habits such as smoking and drinking following retirement.

One airline discovered that customers who preorder a vegetarian meal are more likely to make their flight, with the interpretation that knowledge of a personalized or specific meal awaiting the customer provides an incentive, or establishes a sense of commitment.

Predictive analytics seeks out such predictive connections and then works to see how they may combine together for more precise prediction.

What are the hottest trends in predictive analytics?

There have been many exciting improvements in the core technology of predictive analytics. One is "uplift modeling" (a.k.a. "persuasion modeling"), which predicts influence. ..in order to do influence. The Obama campaign used it to influence voters in the 2012 presidential election; marketing uses it to more adeptly persuade customers; and medicine uses it to better select per-patient treatments. This topic is the focus of the final chapter of this book.

Another hot trend is ensemble models. Like the collective intelligence that spawns the wisdom of a crowd of people, we see the same effect with a crowd of predictive models. Each model alone may be fairly primitive such as a few simple rules, so it gets prediction wrong a lot, as an individual person trying to predict also does. But have them come together as a group and there emerges a new level of predictive performance.

Does the NSA use predictive analytics, and how does that impact the amount of data collected on us?

It's a foregone conclusion that the world's largest spy organization employing the world's largest number of Ph.D. mathematicians considers predictive analytics a strategic priority. Predictive analytics realizes a great potential for law enforcement: The automatic discovery of new suspects. The value of this capability multiplies the incentive to collect increasing amounts of data about civilians. The NSA needs data about everyone, including those of us with no connection to crime whatsoever—not to spy on us but to establish a quantitative baseline. This in turn only amplifies the stakes of the contentious security-versus-privacy debate.

What is the coolest thing predictive analytics has done?

One of the most inspirational accomplishments of predictive analytics is IBM's "Jeopardy!"-playing Watson computer, which triumphed against the all-time human champions on the TV quiz show. The questions can be about most any topic, are intended for humans to answer, and can be complex grammatically. It turns out that predictive modeling is the way in which Watson succeeds in determining the answer to a question: it predicts, "Is this candidate answer the correct answer to this question?" It knocks off one correct answer after another—incredible.

What are companies predicting about me as a customer?

Here are just a few examples:

- Facebook predicts which of 1,500 candidate posts (on average) will be most interesting to you in order to personalize your ordered news feed.

"The most readable (for we laymen) 'big data' book I've come across. By far. Great vignettes/stories."

—Tom Peters, co-author of "In Search of Excellence"

"An operating manual for twenty-first-century life. Drawing predictions from big data is at the heart of nearly everything, whether it's in science, business, finance, sports, or politics. And Eric Siegel is the ideal guide."

—Stephen Baker, author of "The Numerati and Final Jeopardy: The Story of Watson, the Computer That Will Transform Our World"

As a professional with extensive operations & development background I wish I could have read this book when I began my journey into Data Science. I am someone who has used and built traditional business intelligence tools over the last fifteen years this book is fantastic at framing how Predictive Analytics is being used and for what specific business benefits.

The book is intentionally not filled with math formulas (which may turn off some) but it focuses more on use cases of how the businesses around you are leveraging the data they already collect through daily operations. It's about how they are gaining a better insight into where their efforts are best spent to maximize their return on investment or capitalize on a previously masked rich subset of their existing customer base.

If you're looking for a technical breakdown of how these algorithms work or are applied there are dozens of other books that Eric recommends as followup (referenced in probably the best notes section of any book I've ever seen).

If you want a taste of the kind of information that you'll find in the book you should look on the Predictive Analytic World website for his keynote speech he did in Boston last year. It's a great book overview and convinced me to purchase the book.

I do (and teach) predictive analysis for a living and love this book, not because it has technical advice that I will use, but instead because it is a *beautifully* written introduction that I can give to people who have no technical background. It is a great book for a high school senior who is thinking about going into any applied mathematical area (like economics or biostatistics). If you have expertise in other areas and if you keep hearing about topics like machine learning or artificial intelligence, IBM Watson or the Netflix Prize and if you want to get a feel for the area this is the book for you.

The quotes and examples frequently tie back into financial modeling but every domain is touched. Rather than being a book on math, this is equal parts history and social science. So, this book will be enjoyable for a wide audience.

As time has gone by, I've found myself going back again and again to refer to specific points discussed in this book. It was a bit heavy at first, thick with facts that I found irritating and contradictory to certain favorite and closely held biases of mine, but over time, I could see his points better and better, in spite of myself.Life isn't fair, and people certainly aren't. The ways that they react to things reflects this to a degree that would surprise even the coldest eyed cynic, and there it is- the thing that bothered me so much....but it's best if you face it. There are some pleasant discoveries in here too, but I think the most important aspect is illusion busting. Those sweet daydreams about how things should be, might be exactly what is holding you back.Forewarned is forearmed, and the information in here is of a hefty caliber. Use it well.Yes, I did actually buy this book, and it was worth every penny.

This book is a great introduction to how organizations use data about you, often provided by you, to determine your behavior. It talks about the many different areas that predictive analytics are used in from advertising to health care. If you don't understand why google or facebook are free you should read this book. If you have privacy concerns about those organizations you should read this book.

If you already have a decent understanding of predictive analytics this book may not be what you are looking for but it does provide information on a number of sources do delve deeper into the subject. This book deepened my knowledge of predictive analytics and pointed me to a number of sources that I am checking out to learn even more.

In respect of full disclosure I have known Eric for years in his capacity as founder of the Predictive Analytics World conference, and in my work in data mining and predictive analytics. That having been said, this is an excellent book for anyone who wants to learn what predictive analytics is, and how predictive analytics may be deployed across a wide range of disciplines. If you are looking for a hardcore set of algorithms or code examples this is not the book for you, and other reviewers have commented on that. I don't think that was the point of Eric's work. Eric's work does provide a review of what I think are the main pillars of predictive analytics; data, modeling, ensembles, uplift, unstructured data, deployment and ethics. If I had an issue with this book it would be in the ordering of the chapters, but, that is my personal view, and I can see why the book was structured the way that it was. The book will help you understand the major themes of predictive analytics, written in a way that let's the reader focus on the outcome, the advantages and the possibilities around predictive analytics. It is an 'easy' read yet still contains valuable insights. If you want to understand what people are talking about when they are talking about predictive analytics, read this book.